Zero-preserving imputation of single-cell RNA-seq data - Linderman, George C.; Zhao, Jun; Roulis, Manolis; Bielecki, Piotr; Flavell, Richard A.; Nadler, Boaz; Kluger, Yuval (Nature communications, 2022)
A key challenge in analyzing single cell RNA-sequencing data is the large number of false zeros, where genes actually expressed in a given cell are incorrectly measured as unexpressed. We present a method based on low-rank matrix approximation which imputes these values while preserving biologically non-expressed genes (true biological zeros) at zero expression levels. We provide theoretical justification for this denoising approach and demonstrate its advantages relative to other methods on simulated and biological datasets. Missing values in scRNA-seq datasets can bias their analysis. Here, the authors threshold the low rank approximation of the expression matrix, so false zeros can be imputed while true zeros are preserved.